Detecting Periodic Biases in Wearable-Based Illness Detection Models

Wearable health devices have revolutionized our ability to continuously analyze human behavior and build longitudinal statistical models around illness by measuring physiological indicators like heart rate over several months of an individual's life. Shifts in these indicators have been correlated with the onset of illnesses such as COVID-19, leading to the development of Wearable-Based Illness Detection (W-BID) models that aim to detect the onset of illness. While W-BID models accurately detect illness, they often over-predict illness during healthy time periods due to variance caused by seemingly random human choices. However, it is because W-BID models treat each input window as independent and identically distributed samples that we are unable to account for the weekly structure of variance that causes false positives. Towards preventing this, we propose a system for identifying structural variance in wearable signals and measuring the effect they have on W-BID models. We demonstrate how a simple statistical model that does not account for weekly structure is strongly biased by weekly structure, with a Pearson correlation coefficient of 0.899.